Compilers that learn to optimise: a probabilistic machine learning approach

Compiler optimisation is the process of making a compiler produce better code, i.e. code that, for example, runs faster on a target architecture. Although numerous program transformations for optimisation have been proposed in the literature, these transformations are not always beneficial and they can interact in very complex ways. Traditional approaches adopted by compiler writers fix the order of the transformations and decide when and how these transformations should be applied to a program by using hard-coded heuristics. However, these heuristics require a lot of time and effort to construct and may sacrifice performance on programs they have not been tuned for.This thesis proposes a probabilistic machine learning solution to the compiler optimisation problem that automatically determines "good" optimisation strategies for programs. This approach uses predictive modelling in order to search the space of compiler transformations. Unlike most previous work that learns when/how to apply a single transformation in isolation or a fixed-order set of transformations, the techniques proposed in this thesis are capable of tackling the general problem of predicting "good" sequences of compiler transformations. This is achieved by exploiting transference across programs with two different techniques: Predictive Search Distributions (PSD) and multi-task Gaussian process prediction (multi-task GP). While the former directly addresses the problem of predicting "good" transformation sequences, the latter learns regression models (or proxies) of the performance of the programs in order to rapidly scan the space of transformation sequences.Both methods, PSD and multi-task GP, are formulated as general machine learning techniques. In particular, the PSD method is proposed in order to speed up search in combinatorial optimisation problems by learning a distribution over good solutions on a set of problem in¬ stances and using that distribution to search the optimisation space of a problem that has not been seen before. Likewise, multi-task GP is proposed as a general method for multi-task learning that directly models the correlation between several machine learning tasks, exploiting the shared information across the tasks.Additionally, this thesis presents an extension to the well-known analysis of variance (ANOVA) methodology in order to deal with sequence data. This extension is used to address the problem of optimisation space characterisation by identifying and quantifying the main effects of program transformations and their interactions.Finally, the machine learning methods proposed are successfully applied to a data set that has been generated as a result of the application of source-to-source transformations to 12 C programs from the UTDSP benchmark suite

Auto-Calibration of WIM Using Traffic Stream Characteristics

This project evaluates the performance of Weigh-in-Motion (WIM) auto-calibration methods used by the Arkansas Department of Transportation (ARDOT). Typical auto-calibration algorithms compare the WIM measured weights of vehicles from the traffic stream to reference values, using five-axle tractor-trailer configured trucks for comparisons, e.g. Federal Highway Administration (FHWA) Class 9. Parameters of the existing algorithms including the Front Axle Weight (FAW) reference value, the sampling frequency required to update the calibration factor, and thresholds for Gross Vehicle Weight (GVW) bins were evaluated. The primary metric used to estimate algorithm performance was Mean Absolute Percent Error (MAPE) between the WIM and static scale GVW measurements. Two altered auto-calibration algorithms based on methodologies utilized by ARDOT and the Minnesota DOT (MNDOT) were developed. Parameters for the algorithms are based on combinations that produced minimal MAPE at the study sites. WIM data from two sites (Lamar and Lonoke) and static scale data from one site (Alma) were collected along Eastbound I-40 in Arkansas during March 2018. The updated MNDOT auto-calibration algorithm reduced the MAPE by 2.5% compared to the baseline method at the Lamar site (n = 77 trucks) and by 1.1% for the Lonoke site (n = 44 trucks). The updated ARDOT algorithm reduced MAPE by 1.6% at the Lamar site and 0.6% at the Lonoke. Due to limitations of the field data collection methodology, the thresholds defining FAW reference values and the FAW reference values themselves were not able to be tested for spatial transferability, e.g. the samples of trucks at the Lonoke WIM site were a subsample of the trucks at the Lamar WIM site. Improvements in auto-calibration accuracy at low volume sites but was not tested due to the small number of confirmed vehicle matches at a third WIM site (Bald Knob, n = 2 trucks). Overall, site specific tuning of auto-calibration algorithms will improve the accuracy of WIM data which is used for pavement design, maintenance programming, and traffic forecasting. For example, improvements of 2.5% MAPE of WIM measured GVW results in a 39% difference in the estimated Equivalent Single Axle Load (ESAL) factors used for pavement design

Essays on Marginal Treatment Effects

In this dissertation, I discuss identification problems with two sources of endogeneity. In both chapters, one source of endogeneity is self-selection into treatment. To address this problem, I use the Marginal Treatment Effect framework developed by Heckman and Vytlacil (2009). In the first chapter, I partially identify the marginal treatment effect (MTE) function when the treatment variable is misclassified. To do so, I explore three sets of restrictions on the relationship between the instrument, the misclassified treatment and the correctly measured treatment, allowing for dependence between the instrument and the misclassification decision. If the signs of the derivatives of the correctly measured propensity score and the mismeasured one are the same, I identify the sign of the MTE function at every point in the instrument\u27s support. If those derivatives are close to each other, I bound the MTE function. Finally, by imposing a functional restriction between those two propensity scores, I derive sharp bounds around the MTE function and any weighted average of the MTE function. To illustrate the usefulness of my partial identification method, I analyze the impact of alternative sentences --- e.g., fines or community services --- on recidivism using random assignment of judges within Brazilian court districts. In this context, misclassification is an issue when the researcher measures the treatment based solely on trial judge\u27s rulings, ignoring that the Appeals Court may reverse sentences. I show that, when I use the trial judge\u27s rulings as my misclassified treatment variable, the misclassification bias may be as large as 10\% of the MTE function, which can be estimated using the final ruling in each case as my correctly measured treatment variable. Moreover, I show that the proposed bounds contain the MTE function in this empirical example. In the second chapter, my coauthors and I present identification results for the marginal treatment effect (MTE) when there is sample selection. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and derive uniformly sharp bounds on this parameter under three increasingly restrictive sets of assumptions. The first result imposes standard MTE assumptions with an unrestricted sample selection mechanism. The second set of conditions imposes monotonicity of the sample selection variable with respect to treatment, considerably shrinking the identified set. Finally, we incorporate a stochastic dominance assumption which tightens the lower bound for the MTE. Our analysis extends to discrete instruments. The results rely on a mixture reformulation of the problem where the mixture weights are identified, extending Lee\u27s (2009) trimming procedure to the MTE context. We propose estimators for the bounds derived and use data made available by Deb et al. (2006) to empirically illustrate the usefulness of our approach

Ансамблевий класифікатор на основі бустінгу

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник роботи: д.т.н., професор, зав. кафедри авіаційних комп’ютерно-інтегрованих комплексів, Синєглазов Віктор МихайловичThis paper considers the construction of a classifier based on neural networks, nowadays AI is a major global trend, as an element of AI, as a rule, an artificial neural network is used. One of the main tasks that solves the neural network is the problem of classification. For a neural network to become a tool, it must be trained. To train a neural network you must use a training sample. Since the marked training sample is expensive, the work uses semi-supervised learning, to solve the problem we use ensemble approach based on boosting. Speaking of unlabeled data, we can move on to the topic of semi-supervised learning. This is due to the need to process hard-to-access, limited data. Despite many problems, the first algorithms with similar structures have proven successful on a number of basic tasks in applications, conducting functional testing experiments in AI testing. There are enough variations to choose marking, where training takes place on a different set of information, the possible validation eliminates the need for robust method comparison. Typical areas where this occurs are speech processing (due to slow transcription), text categorization. Choosing labeled and unlabeled data to improve computational power leads to the conclusion that semi-supervised learning can be better than teacher-assisted learning. Also, it can be on an equal efficiency factor as supervised learning. Neural networks represent global trends in the fields of language search, machine vision with great cost and efficiency. The use of "Hyper automation" allows the necessary tasks to be processed to introduce speedy and simplified task execution. Big data involves the introduction of multi-threading, something that large companies in the artificial intelligence industry are doing.У даній роботі розглядається побудова класифікатора на основі нейронних мереж, на сьогоднішній день AI є основним світовим трендом, як елемент AI, як правило, використовується штучна нейронна мережа. Однією з основних задач, яку вирішує нейронна мережа, є проблема класифікації. Щоб нейронна мережа стала інструментом, її потрібно навчити. Для навчання нейронної мережі необхідно використовувати навчальну вибірку. Оскільки позначена навчальна вибірка є дорогою, у роботі використовується напівконтрольоване навчання, для вирішення проблеми ми використовуємо ансамблевий підхід на основі бустингу. Говорячи про немарковані дані, ми можемо перейти до теми напівконтрольованого навчання. Це пов’язано з необхідністю обробки важкодоступних обмежених даних. Незважаючи на багато проблем, перші алгоритми з подібними структурами виявилися успішними в ряді основних завдань у додатках, проводячи експерименти функціонального тестування в тестуванні ШІ. Є достатньо варіацій для вибору маркування, де навчання відбувається на іншому наборі інформації, можлива перевірка усуває потребу в надійному порівнянні методів. Типовими областями, де це відбувається, є обробка мовлення (через повільну транскрипцію), категоризація тексту. Вибір мічених і немічених даних для підвищення обчислювальної потужності призводить до висновку, що напівкероване навчання може бути кращим, ніж навчання за допомогою вчителя. Крім того, воно може мати такий же коефіцієнт ефективності, як навчання під наглядом. Нейронні мережі представляють глобальні тенденції в області мовного пошуку, машинного зору з великою вартістю та ефективністю. Використання «Гіперавтоматизації» дозволяє обробляти необхідні завдання для впровадження швидкого та спрощеного виконання завдань. Великі дані передбачають впровадження багатопоточності, чим займаються великі компанії в індустрії штучного інтелекту

Aeronautical Engineering: A continuing bibliography with indexes (supplement 166)

This bibliography lists 558 reports, articles and other documents introduced into the NASA scientific and technical information system in September 1983

Aeronautical engineering: A continuing bibliography with indexes (supplement 195)

This bibliography lists 389 reports, articles and other documents introduced into the NASA scientific and technical information system in December 1985

University of Windsor Graduate Calendar 2002-2004

https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1019/thumbnail.jp

ATHENA Research Book

The ATHENA European University is an alliance of nine Higher Education Institutions with the mission of fostering excellence in research and innovation by facilitating international cooperation. The ATHENA acronym stands for Advanced Technologies in Higher Education Alliance. The partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal, and Slovenia: the University of Orléans, the University of Siegen, the Hellenic Mediterranean University, the Niccolò Cusano University, the Vilnius Gediminas Technical University, the Polytechnic Institute of Porto, and the University of Maribor. In 2022 institutions from Poland and Spain joined the alliance: the Maria Curie-Skłodowska University and the University of Vigo. This research book presents a selection of the ATHENA university partners' research activities. It incorporates peer-reviewed original articles, reprints and student contributions. The ATHENA Research Book provides a platform that promotes joint and interdisciplinary research projects of both advanced and early-career researchers